Let’s read Julia documentation in your preferred language
SatoshiTerasaki@AtelierArith
Link to this slide
https://atelierarith.github.io/AtelierArith_JuliaCon2025_talk/slide/slide.html#/title-slide
Overview
- Background and Challenges
- Introduction to DocstringTranslation.jl
- Multilingual translation of Julia documentation
- Running demonstration (if possible)
Background and Challenges
- Julia allows us to write code with high-level operations without losing execution performance (solving two-language problem)
- I want more people to use Julia
Background and Challenges
When you want to learn something about Julia, we need to do before reading Julia’s official documentation.
This requirements may be non-trivial for someone like me.
if you in Set(NativeEnglishSpeakers)
println("No problem :D.")
elseif you ∈ Set(GoodAtReadingEnglish)
println("It's Okay.")
else
# This block happens if `you` is `me`
lang = native_language(you) # e.g., Japanese
@assert lang ≠ "English"
println("Meh.")
println("It takes a lot of time to understand correctly in English.")
println("I want to read documentation in $(lang).")
endChallenges (in Japan)
- For Japanese, learning English is not easy than English native speakers expected.
- See this discussion
- The number of Japanese who use English daily is limited
- We usually read, write, listen and discuss something in Japanese
Challenges (in Japan)
- Threfore many technical documents are expected to be readable in Japanese
- This is a key factor for making Japanese try and use something(=Julia)
- To grow the Julia community in Japan, I think we need documentation written in Japanese. That’s why I created DocstringTranslation.jl package.
Solution
- Manual translation for Julia documents requires significant time
- My life is too short
- Developing an automated translation system utilizing machine translation
What is DocstringTranslation.jl?
A package for translating Julia docstrings using OpenAI API
julia> @doc exp
exp(x)
Compute the natural base exponential of x, in other words ℯ^x.
See also exp2, exp10, and cis.
Examples
≡≡≡≡≡≡≡≡
julia> exp(1.0)
2.718281828459045
julia> exp(im * pi) ≈ cis(pi)
true
exp(A::AbstractMatrix)
Compute the matrix exponential of A, defined as:
e^A = \sum_{n=0}^{\infty} \frac{A^n}{n!}.
For symmetric or Hermitian matrices A, an eigendecomposition (eigen) is used; otherwise, the scaling and squaring algorithm
(see [^H05]) is employed.
│ [^H05]
│
│ Nicholas J. Higham, "The squaring and scaling method for the matrix exponential revisited", SIAM Journal
│ on Matrix Analysis and Applications, 26(4), 2005, 1179-1193. doi:10.1137/090768539
│ (https://doi.org/10.1137/090768539)
Examples
≡≡≡≡≡≡≡≡
julia> A = Matrix(1.0I, 2, 2)
2×2 Matrix{Float64}:
1.0 0.0
0.0 1.0
julia> exp(A)
2×2 Matrix{Float64}:
2.71828 0.0
0.0 2.71828What is DocstringTranslation.jl?
A package for translating Julia docstrings using OpenAI API
julia> ENV["OPENAI_API_KEY"] = "sk-<blah>"
julia> using DocstringTranslation
julia> @switchlang! :ja
julia> @doc exp
exp(x)
xの自然基底指数を計算します。言い換えれば、ℯ^xです。
他にexp2、exp10、およびcisも参照してください。
例
≡≡
julia> exp(1.0)
2.718281828459045
julia> exp(im * pi) ≈ cis(pi)
true
exp(A::AbstractMatrix)
行列 A の行列指数関数を計算します。定義は次の通りです。
e^A = \sum_{n=0}^{\infty} \frac{A^n}{n!}.
対称行列またはエルミート行列 A
の場合は、固有分解（eigen）が使用され、それ以外の場合はスケーリングと平方化アルゴリズム（[^\H05]を参照）が選択されます。
│ [^H05]
│
│ Nicholas J. Higham, "The squaring and scaling
│ method for the matrix exponential revisited",
│ SIAM Journal on Matrix Analysis and
│ Applications, 26(4), 2005, 1179-1193.
│ doi:10.1137/090768539
│ (https://doi.org/10.1137/090768539)
例
≡≡
julia> A = Matrix(1.0I, 2, 2)
2×2 Matrix{Float64}:
1.0 0.0
0.0 1.0
julia> exp(A)
2×2 Matrix{Float64}:
2.71828 0.0
0.0 2.71828What is DocstringTranslation.jl?
A package for translating Julia docstrings using OpenAI API
@doc exp (Original)
julia> @doc exp
exp(x)
Compute the natural base exponential of x, in other words ℯ^x.
See also exp2, exp10, and cis.
Examples
≡≡≡≡≡≡≡≡
julia> exp(1.0)
2.718281828459045
julia> exp(im * pi) ≈ cis(pi)
true
exp(A::AbstractMatrix)
Compute the matrix exponential of A, defined as:
e^A = \sum_{n=0}^{\infty} \frac{A^n}{n!}.
For symmetric or Hermitian matrices A, an eigendecomposition (eigen) is used; otherwise, the scaling and squaring algorithm
(see [^H05]) is employed.
│ [^H05]
│
│ Nicholas J. Higham, "The squaring and scaling method for the matrix exponential revisited", SIAM Journal
│ on Matrix Analysis and Applications, 26(4), 2005, 1179-1193. doi:10.1137/090768539
│ (https://doi.org/10.1137/090768539)
Examples
≡≡≡≡≡≡≡≡
julia> A = Matrix(1.0I, 2, 2)
2×2 Matrix{Float64}:
1.0 0.0
0.0 1.0
julia> exp(A)
2×2 Matrix{Float64}:
2.71828 0.0
0.0 2.71828@doc exp (Translated into Japanese)
julia> ENV["OPENAI_API_KEY"] = "sk-<blah>"
julia> using DocstringTranslation
julia> @switchlang! :ja
julia> @doc exp
exp(x)
xの自然基底指数を計算します。言い換えれば、ℯ^xです。
他にexp2、exp10、およびcisも参照してください。
例
≡≡
julia> exp(1.0)
2.718281828459045
julia> exp(im * pi) ≈ cis(pi)
true
exp(A::AbstractMatrix)
行列 A の行列指数関数を計算します。定義は次の通りです。
e^A = \sum_{n=0}^{\infty} \frac{A^n}{n!}.
対称行列またはエルミート行列 A
の場合は、固有分解（eigen）が使用され、それ以外の場合はスケーリングと平方化アルゴリズム（[^\H05]を参照）が選択されます。
│ [^H05]
│
│ Nicholas J. Higham, "The squaring and scaling
│ method for the matrix exponential revisited",
│ SIAM Journal on Matrix Analysis and
│ Applications, 26(4), 2005, 1179-1193.
│ doi:10.1137/090768539
│ (https://doi.org/10.1137/090768539)
例
≡≡
julia> A = Matrix(1.0I, 2, 2)
2×2 Matrix{Float64}:
1.0 0.0
0.0 1.0
julia> exp(A)
2×2 Matrix{Float64}:
2.71828 0.0
0.0 2.71828